3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. X-1/W1-2023; pp. 1131 - 1136
• Main Authors: Morsy, S., Yánez Suárez, A. B., Robert, K.
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 01-01-2023; Copernicus Publications
• Subjects: Algorithms; Classifiers; Corals; Data acquisition; Ecosystem conservation; Ecosystems; Habitats; Image reconstruction; Machine learning; Mapping; Marine ecosystems; Photogrammetry; Sponges; Strategic management; Three dimensional models; Workflow
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-X-1-W1-2023-1131-2023

Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances, using machine learning (ML) algorithms with structure-from-motion (SfM) photogrammetry has become a promising approach for mapping benthic habitats accurately and at very high resolutions. This paper explores using SfM photogrammetry and extreme gradient boosting (XGBoost) classifier for benthic habitat 3D mapping of a vertical wall at the Charlie-Gibbs Fracture Zone in the North Atlantic Ocean. The classification workflow started with extracting frames from video footage. The SfM was then applied to reconstruct the 3D point cloud of the wall. Thereafter, nine geometric features were derived from the 3D point cloud geometry. The XGBoost classifier was then used to classify the vertical wall into rock, sponges, and corals (Case 1 - three classes). In addition, we separated the sponges class into three types of sponges: Demospongiae, Hexactinellida, and other Porifera (Case 2 - five classes). Moreover, we compared the results from XGBoost with the widely used ML classifier, random forest (RF). For Case 2, XGBoost achieved an overall accuracy (OA) of 74.45%, while RF achieved 73.10%. The OA improved by about 10% from both classifiers when the three types of sponges were combined into one class (Case 1). Results showed that the presented 3D mapping of benthic habitat has the potential to provide more detailed and accurate information about marine ecosystems.